1983Journal of Marketing ResearchRequires access

Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data

Jordan J. Louviere, George G. Woodworth

Open publisher page 1,412 citations

Abstract

The authors integrate concepts in conjoint analysis and discrete choice theory in econometrics to develop a new approach to the design and analysis of controlled consumer choice or resource allocation experiments. The article is concerned with estimating the parameters of conjoint-type functions from discrete choice or allocation data. Emphasis is placed on the multinomial logit model and aggregate choice or allocation data to illustrate the concepts in a series of empirical examples ranging from simple to complex. The authors present limited external validity evidence to support the approach and make comparisons with traditional conjoint approaches.

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What this paper is about

The authors integrate concepts in conjoint analysis and discrete choice theory in econometrics to develop a new approach to the design and analysis of controlled consumer choice or resource allocation experiments. The article is concerned with estimating the parameters of conjoint-type functions from discrete choice or allocation data. Emphasis is placed on the multinomial logit model and aggregate choice or allocation data to illustrate the concepts in a series of empirical examples ranging from simple to complex. The authors present limited external validity evidence to support the approach and make comparisons with traditional conjoint approaches.

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Available abstract

The authors integrate concepts in conjoint analysis and discrete choice theory in econometrics to develop a new approach to the design and analysis of controlled consumer choice or resource allocation experiments. The article is concerned with estimating the parameters of conjoint-type functions from discrete choice or allocation data. Emphasis is placed on the multinomial logit model and aggregate choice or allocation data to illustrate the concepts in a series of empirical examples ranging from simple to complex. The authors present limited external validity evidence to support the approach and make comparisons with traditional conjoint approaches.

Key concepts: Multinomial logistic regression, Discrete choice, Aggregate (composite), Conjoint analysis, Consumer choice, Mixed logit, Econometrics, Computer science

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